Top Machine Learning Jobs in Warsaw

As a Senior Python Engineer, you'll design and implement scalable solutions, collaborate across teams, and manage infrastructure on cloud platforms, ensuring high-quality deliverables.

Design, build, and productionize ML/NLP/LLM systems for clinical and financial analytics. Tune NLP engines, develop statistical and regression models, create private LLMs and RAG pipelines, deploy models on Databricks/Fabric/Azure ML, and build the enterprise AI analytics chatbot with monitoring and CI/CD.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

The ML Ops/Data Engineer will ensure reliability and scalability of ML systems, manage data pipelines, monitor performance, and collaborate with teams for optimization.

Design, build, and maintain ML models and production pipelines, lead experimentation, collaborate with engineers and product, monitor model performance, and document processes while staying current on ML/AI advances.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

Design, prototype, and productionize ML models for personalization in mobile games. Build experiments and A/B tests, monitor player data, collaborate with engineering and product teams, and document work to improve retention, revenue, and player experience.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Design and implement production-grade machine learning components for a next-generation bidder engine in AdTech. Collaborate with software engineers and architects to integrate ML into real-time bidding systems, replace rule-based logic with ML-driven decisioning, optimize performance, support deployment and monitoring, participate in architecture and design reviews, and mentor peers while driving operational excellence.

Join the MLOps team to deploy, maintain, monitor, and optimize machine learning models in production. Build data and model pipelines, implement CI/CD for ML, ensure model performance and cost efficiency, and collaborate cross-functionally to deliver scalable AI solutions.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.